In this paper, we investigated an Inventory Routing Problem (IRP) with deterministic customer demand, in a two-tiered supply chain. The supply chain network consists of a supplier who uses a single vehicle with a given capacity to deliver a single type of product to many customers. We are interested in population-based algorithms to solve our problem. A Memetic Algorithm (MA) is developed based on Genetic Algorithm (GA) and Variable Neighborhood Search methods (VNS). The proposed metaheuristics are tested on small and large sizes referenced benchmarks. The results of MA are compared with those of classical GA and with the optimal solutions from the literature. The comparison showed the efficiency of the MA use and its ability to generate high quality solutions within a reasonable time.

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In a supply chain, inventory is the single largest source of costs for a company. This is due to the various physical and informational activities that accompany inventory management, primarily the holding and transportation of inventory. Companies are looking to streamline these activities and minimize the associated costs. One of the most coveted models for jointly solving these two problems is the Inventory Routing Problem (IRP), which will be the focus of this study. This paper deals with the case of a deterministic replenishment demand in a distribution network consisting of a supplier and a number of customers to be served by a single vehicle over a finite planning horizon. We study the impact of increasing supplier delivery times on the network costs. A mathematical model is developed and solved by an exact method. The results obtained will be analyzed and solutions will be suggested.